Electrical energy generators, including, for example, solar or photovoltaic (PV) generator panels and wind turbines, are becoming increasingly desirable for electricity consumers around the world. Using these generators, consumers can passively extract value from ambient weather and climate conditions to reduce their reliance on power generated by other sources. Consumers also find these generators attractive to reduce their utility bills. Although not all areas are well-suited for solar or wind power generation due to geography, weather, and climate conditions, consumers in the areas in which these energy sources are abundant can realize substantial savings in electricity and reduced dependence on the grid.
Some factors limiting the adoption of solar and other renewable energy sources include the cost of purchasing and installing the generators and the uncertainty in determining how much utility savings will be realized. Meteorological conditions such as storms, cloudy skies, and high or low winds can have a substantial impact on the amount of energy produced by the generators. Thus, these generators may be referred to as being intermittent or conditional-output generators due to their intermittent and inconsistent production, and there is uncertainty about how much a given generator will produce over time.
Solar panel providers have developed effective models for estimating average annual production of a solar panel positioned in various geographic locations, and they can estimate the value generated by a solar panel on an annual basis with relative certainty since long-term weather trends are, on average, consistent. Daily or hourly weather conditions are, however, much more unpredictable, especially far in advance. For example, while a meteorologist may be able to predict with a high degree of certainty that it will be cloudy during the short duration of 12:00 p.m. to 12:15 p.m. tomorrow or the general high and low temperature in a month from now, there is a high degree of uncertainty as to whether it will specifically be cloudy between 12:00 p.m. to 12:15 p.m. in two, six, or nine months from now.
Energy consumption management industries and consumption management system (CMS) providers are challenged by this uncertainty. A CMS, for example, may be used to reduce “peak demand charges” that are charged to customers based on the customer's peak average demand (e.g., in kW) over a short division of a billing period, such as over a 15-minute span of time in a 30-day billing cycle. The average demand over these short time periods is the net average consumption that is metered by the utility provider, so power contributed from any grid-independent energy sources (e.g., the customer's generators or energy storage systems) can reduce the average. A CMS may monitor the overall consumption of the site and discharge energy from an energy storage device to prevent the net metered consumption from exceeding a predetermined setpoint, thereby limiting peak demand charges by preventing the average consumption from increasing beyond the setpoint. In many cases, the CMS may use load prediction algorithms to anticipate the customer's average demand.
The unpredictability of intermittent generators makes predicting the customer's net consumption difficult as well. Precise predictions are important to energy consumption management industries since peak demand charges can be based on just one short peak demand charge measuring interval, and inaccuracies and imprecision during that one measuring interval that cause an unwanted peak in consumption can wipe out any benefits gained by used the CMS throughout the rest of a billing period. For example, the CMS may anticipate a normal load and a normal contribution of energy from a solar panel and from a battery source to offset that load and to keep the net load below a setpoint. If, however, even an unexpected 10-minute change of weather causes the solar panel to lose half of its production, the net load may spike, potentially in a manner that cannot be compensated for additional energy contribution by the CMS.
Because of these problems with predicting detailed performance of intermittent generators, energy consumption management industries are unable to guarantee peak demand charge savings that would be produced by the intermittent generators. Accordingly, there is a need for improvements in the prediction and simulation of load profiles generated by intermittent generators and for improvements in the ways that intermittent generators are assessed for implementation by utility customers.